AMIEOD combines a multi-expert enhancement module with detection-guided regression and selection losses to raise object detection accuracy in low-illumination images.
Faster r-cnn: Towards real-time object detection with region proposal networks
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A ResNet50 OOD filter plus YOLOv8/11/12 pipeline reaches 99.77% OOD rejection accuracy and 0.947 mAP on mammograms while blocking irrelevant imaging inputs.
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AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes
AMIEOD combines a multi-expert enhancement module with detection-guided regression and selection losses to raise object detection accuracy in low-illumination images.
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Analysis of Invasive Breast Cancer in Mammograms Using YOLO, Explainability, and Domain Adaptation
A ResNet50 OOD filter plus YOLOv8/11/12 pipeline reaches 99.77% OOD rejection accuracy and 0.947 mAP on mammograms while blocking irrelevant imaging inputs.